Four pitfalls in normalizing citation indicators: An investigation of ESI’s selection of highly cited papers

InCites Essential Science Indicators is becoming increasingly used to identify top-performing research and evaluate the impact of institutes. Unfortunately, our study shows that ESI indicators, as well as other normalized citation indicators, have the following flaws. First, the publication month and the online-to-print delay affect a paper’s probability of becoming a Highly Cited Paper (HCP). Papers published in the earlier months of the year are more likely to accumulate enough citation counts to rank at the top 1% compared with those published in later months of the year. Papers with longer online-to-print delays have an apparent advantage for being selected as HCPs. Research field normalizations lead to the third pitfall. Different research fields have different citation thresholds for HCPs, making research field classification important for a journal. In addition, the uniform thresholds for both articles and reviews in ESI affect the reliability of HCP selection because, on average, reviews tend to have higher citation rates than articles. ESI’s selection of HCPs provides an intuitive feel for the problems of normalized citation impact indicators, such as those provided in InCites and SciVal.     

Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.     

Seeking Nettle Textiles – Utilizing a Combination of Microscopic Methods for Fibre Identification

Bast fibres have been commonly used as a textile material in Northern Europe since Neolithic times. However, the process of identifying the different species has been problematic, and many important questions related to their cultural history are still unanswered. For example, a modified Herzog test and the presence of calcium oxalate crystals have both been used in identification. In order to generate more reliable results, further research and advancement in multi-methodological methods is required. This paper introduces a combination of methods which can be used to identify and distinguish flax (Linum usitatissimum), hemp (Cannabis sativa), and stinging nettle (Urtica dioica). The research material consisted of reference fibres and 25 fibre samples obtained from 12 textiles assumed to be made of nettle. The textiles were from the Finno-Ugric and Historical Collections of The National Museum of Finland. The fibre samples were studied by observing the surface characteristics and cross sections with transmitted light microscopy, and by using a modified Herzog test with polarized light, in order to identify the distinguishable features in their morphological structures. The study showed that five out of 25 samples were cotton, 16 nettle, one flax, and one hemp. Findings from two samples were inconsistent. The results show that it is possible to distinguish common north European bast fibres from each other by using a combination of microscopic methods. Furthermore, by utilizing these combined methods, new and more reliable information could be obtained from historical ethnographic textiles, which creates new vistas for the interpretation of their cultural history.     

网络舆情场中信息受众观点测度的机理及路径分析

[目的/意义]整合定性与定量的舆情研究视角,统一多模态研究对象,实现网络舆情信息受众的观点测度,可修复网络舆情分析与治理的理论与实践裂痕。[方法/过程]通过引入网络舆情场的概念,对网络舆情信息受众、受众观点测度的内涵进行诠释,就网络舆情场与信息受众观点测度的逻辑关系加以辩证讨论。[结果/结论]提出网络舆情场内舆情受众观点测度机理和具体测度路径,为后续网络舆情场中的网络舆情信息分析、多维度信息受众观点测度,受众认知规律发现,网络舆情监督和管控限制等层面的研究做相应铺垫。     

基于CNN-SVM 和转发树的微博事件情感分析

基于微博的情感分析近几年获得了广泛的关注,但是通常是对微博上的电影或者产品的评论。我们的研究目标是针对微博上的热点事件的分析,将这些事件的评论分为正向或者负向,将微博用户的评论进行分类有利于辨别公众对于这个事件的普遍看法。本文提出了一个基于卷积神经网络的混合模型：CNN-SVM,用于对事件的评论进行分类。其次,根据微博用户的转发行为,我们提出了一种新的数据结构——转发树,用以解决评论中的一些分类混乱情况。实验结果表明CNN-SVM 确实提高了情感分类的正确率,新提出的转发树结构在对真实世界的情感倾向性的逼近中也是十分有效的。     

对《中图法》（第四版）某些类目细分问题的探讨

本文全面分析了《中图法》四版通过注释说明,运用冒号组配法(包括文字标记法)对某些类目进行细分的方法,并分析了一些类目下文献交错混排的问题。建议扩大冒号组配法(包括文字标记法)的范围,对这些类目进行细分。     

VISION:集成分类法、主题词表和语义元数据的概念网络

本文提出了一种在分类法和主题词表的基础上集成语义元数据、构建概念网络、实现概念检索的方法.和其他的概念检索系统相比,它的最大特色是在检索之前先将信息资源根据其内容和主题组织到概念网络中.这样的概念网络,既是一个资源组织的框架,又是一个知识浏览和概念检索的信息空间.同时,还能支持用户学习.文章介绍了国内外概念检索的研究现状,讨论了集成分类法、主题词表和语义元数据构建概念网络的方法和好处.介绍了一个原型系统VISION,它是在<中国分类主题词表>的基础上,利用北京大学图书馆计算机类的书目数据实现的.文章最后进行深入讨论并介绍下一步的研究工作.     

计算机编目环境下卡片分类目录组织的作用

传统的分类目录是按知识体系揭示馆藏、宣传图书、辅导阅读，供读检索的工具．但在计算机编目环境下．卡片分类目录的报道及检索功能已被计算机检索系统所代替。尽管如此．分类卡片目录组织仍不能取消．因为分类目录组织能发现在分类、编目等环节中出现的问题．起到了校对书目数据的作用，为提高书目数据库的准确性提供了有利的保障。     

含盘图书的分类与管理

随着计算机有普及，图书的出版形式也发生了很大变化，许多图书为了阅读的方便含盘（光盘或软盘）一并发行，这样的图书若按原有管理方法分配、管理，显然不大合理，本文论述了这类含盘图书的分类方法及其管理。     

数字图书馆与数字化分类法

数字化分类法不仅要能够适合传统文献的分类检索 ,也应能适应各种信息尤其是网络信息的分类和检索。它应借鉴搜索引擎构建多维的分类体系和多途径检索 ,吸收我国已有的信息分类编码成果 ,具有自动搜索、分析、标引和著录的智能性。参考文献 6。